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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: CDP Platform Operations | 15% | - Cloudera Data Platform Architecture - Data Lake and Storage - Cloudera Flow Management - Cluster Management and Monitoring |
| Topic 2: Data Pipeline Orchestration | 20% | - Apache Airflow on CDP - Workflow Dependencies - Pipeline Scheduling and Triggers - Error Handling and Retries |
| Topic 3: Data Ingestion and Integration | 20% | - CDC (Change Data Capture) - Stream Data Ingestion - Batch Data Ingestion - Data Federation - Data Transformation and ETL |
| Topic 4: Data Quality and Governance | 15% | - Data Validation and Cleansing - Access Control and Security - Data Lineage - Data Catalog and Metadata |
| Topic 5: Data Processing with Spark | 30% | - Spark Structured Streaming - Spark Core Concepts - Spark SQL and DataFrames - Spark Performance Optimization - DataFrame and Dataset APIs |
Cloudera CDP Data Engineer - Certification Sample Questions:
In Spark, what is the advantage of using the 'coalesce' method over the 'repartition' method when reducing the number of partitions in an RDD?
- A. 'repartition' is incapable of reducing the number of partitions.
- B. 'coalesce' reduces the number of partitions without a full data shuffle, enhancing performance.
- C. 'coalesce' triggers a full shuffle of the data, improving data distribution.
- D. 'coalesce' can increase the number of partitions without shuffling.
Correct Answer: B 🗳️
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When configuring a Hive table with bucketing, how does the choice of the bucketing column(s) influence query performance?
- A. Selecting highly unique or skewed columns for bucketing can lead to uneven data distribution and potential performance bottlenecks.
- B. The choice of bucketing column(s) has no impact on performance as long as the number of buckets is optimally chosen.
- C. Bucketing on columns that are frequently updated optimizes data modification operations and query performance.
- D. The bucketing column(s) should be chosen randomly to ensure a uniform distribution of data across buckets.
Correct Answer: A 🗳️
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How does Spark handle data shuffling during distributed processing?
- A. By transferring only required data between executors
- B. Spark doesn't perform data shuffling
- C. By storing all data on a single node
- D. By broadcasting all data to each executor
Correct Answer: A 🗳️
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Due to regulatory requirements, you need to permanently delete specific sensitive records from an Iceberg table. Which of the following techniques would be most appropriate?
- A. Use Iceberg's EXPIRE SNAPSHOTS procedure to remove snapshots containing the sensitive data.
- B. Implement a custom process, leveraging Iceberg's row-level updates to delete the sensitive records and then using file-level operations to physically remove the data.
- C. Modify the data files directly using low-level tools to overwrite the sensitive data.
- D. Issue a standard Iceberg DELETE query, as deleted data will automatically be expunged from the table.
Correct Answer: B 🗳️
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In a CI/CD pipeline, what is a key consideration when integrating Cloudera Data Engineering (CDE. service API calls for deploying Spark jobs, specifically regarding security?
- A. Incorporating API token rotation and secure secret management practices
- B. Using generic Spark Docker images for all jobs to avoid API customization
- C. Disabling TLS for API endpoints to simplify integration
- D. Hardcoding API keys in the pipeline scripts
Correct Answer: A 🗳️
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